Explore AI Agent Skills & Claude Prompts
Discover open-source agent skills for Claude Code, Codex, ChatGPT, and any tool that uses SKILL.md.
Enter through keywords, occupations, creators, and GitHub sources to see what kinds of skills are emerging across domains.
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Connect 381,784 public skills to your own search, analytics, or agent workflow with the REST API.
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new-command-docs
by tractorjuiceThis skill should be used when a new ArcKit command has been added and documentation needs updating across the repository. Triggers: update documentation for new command, update command count, add command to README, update DEPENDENCY-MATRIX, update docs/index.html, new command documentation checklist, update all docs for new command, add command to dependency matrix, update command tables, sync documentation after adding command, I added a new command what do I update, post-command documentation, new slash command docs.
wardley-mapping
by tractorjuiceThis skill should be used when the user asks about Wardley Mapping, evolution stages, strategic positioning, creating maps, value chain decomposition, gameplay patterns, doctrine assessment, climatic patterns, build vs. buy, or inertia analysis.
architecture-workflow
by tractorjuiceThis skill should be used when the user is starting an architecture project or asking what to run next. Load whenever the task sounds like 'I'm starting a new project', 'guide me through', 'what command should I run', 'what comes next', 'how do I begin', 'help me get started', 'which /arckit:* in what order', 'set up a new project', 'new system build', or 'where do I start'. Recommends a tailored command sequence based on sector, project type, current stage, and timeline.
arckit-build
by tractorjuiceThis skill should be used when the user wants to bulk-build ArcKit artefacts in parallel rather than running individual /arckit:* commands one at a time. Invoke manually with /arckit:arckit-build when the task sounds like 'kick off a build', 'build everything', 'generate all artefacts', 'run all the commands', 'rebuild this project from scratch', 'resume the build', 'pick up where we left off', 'refresh the artefacts', 'run the recipe', 'build the whole project end-to-end', or 'parallel build', or mentions `--plan`, `--resume`, `--target`, `--refresh`, `--recipe`, or `.arckit/state.json`. The skill orchestrates parallel /arckit:* generation using subagent isolation: reads project state, computes the artefact dependency DAG, dispatches one subagent per target per wave (each subagent invokes a /arckit:* skill in its own context), validates outputs, commits the wave, and persists progress to .arckit/state.json for resumability.
mermaid-syntax
by tractorjuiceThis skill should be used when the user asks about Mermaid diagram syntax, how to write flowchart, sequence, class, state, ER, Gantt, C4, mindmap, timeline, or other diagram types, node shapes, styling, theming, or rendering errors.
plantuml-syntax
by tractorjuiceThis skill should be used when the user asks about PlantUML syntax for C4-PlantUML, sequence, class, activity, state, ER, component, deployment, or use case diagrams, rendering errors, layout conflicts, skinparams, or themes.
pr-review-community-overlay
by tractorjuiceUse when reviewing a PR to tractorjuice/arc-kit that adds or extends a community jurisdictional overlay (e.g. au-*, ca-*, uae-*, fr-*, at-*, eu-*, future state-level like au-vic-*) — produces a structured review with blockers, important issues, minor issues, and positive callouts.
auto-memory
by tractorjuiceThis skill should be used when the user asks to 'set up memory', 'create memory files', 'create MEMORY.md', 'set up topic files', 'improve memory organization', 'what should I remember', 'how to structure memory files', 'MEMORY.md is too long', 'what goes in memory vs topic files', 'memory best practices', or wants to establish a persistent knowledge base across Claude Code sessions. Triggers on: memory setup, MEMORY.md, topic files, cross-session knowledge, persistent notes, memory maintenance, memory pruning.
release
by tractorjuiceCut a new ArcKit release — bump versions in lockstep, regenerate non-Claude formats, validate plugin/marketplace agreement, tag, and push to extension repos. Use when the user says 'cut a release', 'release ArcKit', 'ship vX.Y.Z', 'bump the version and release', 'do the release flow', 'tag and publish', or 'push the extensions'. This is a manual, high-consequence workflow: it never runs automatically.
g-cloud-framework-questions
by tractorjuiceThis skill should be used when the user asks about "character limit", "how many features", "what lots", "CCS questions", "service categories", "declaration questions", "word limit", "mandatory exclusion", "service name limit", "pricing format", "what questions does CCS ask", "lot 1 categories", "lot 2 categories", "lot 3 categories", "how many words", "features limit", "benefits limit", "service description limit", "framework questions", "marketplace questions", "G-Cloud question structure", or needs quick lookups on Digital Marketplace field constraints and lot structures.
sfia-skills-day-rates
by tractorjuiceThis skill should be used when the user asks about "SFIA level", "SFIA skills", "day rate", "what level is", "solution architect level", "Lot 3 roles", "DDaT roles", "AI skills framework", "cloud migration roles", "consultancy rates", "SFIA mapping", "role to SFIA", "skill level", "cloud support roles", "what SFIA level", "rate card", "SFIA 8", "junior consultant rate", "senior consultant rate", "principal consultant", "DevOps engineer level", "security engineer level", "project manager SFIA", or needs guidance on SFIA levels, role mappings, day rates, and Lot 3 service team compositions.
cloud-security-compliance
by tractorjuiceThis skill should be used when the user asks about "ISO 27001", "Cyber Essentials", "NCSC principles", "cloud security", "what certifications", "SOC 2", "data protection", "UK GDPR", "security clearance", "PCI DSS", "compliance framework", "CSA STAR", "DSPT", "Technology Code of Practice", "AI Playbook", "what evidence do I need", "security certification", "NHS data", "BPSS", "SC clearance", "DV clearance", "what security do I need", "certification cost", "ISO 22301", or needs guidance on security certifications, compliance requirements, and evidence for G-Cloud submissions.
Browse Agent Skills by Occupation
23 major groups · 867 SOC occupations
Browse by Category
Explore agent skills organized by their primary use case
Explore the agent skills ecosystem by occupation and creator
SkillMD is not just a keyword search box. It is an open map that organizes public skills by occupation, creator, and repository, helping you see which workflows, judgment criteria, and domain habits people are writing for AI agents.
Then follow creators and GitHub repositories back to the source: compare the skills a team maintains, whether the repo is active, and how the README frames the work before you open, install, or reuse anything.
Use it three ways: learn an unfamiliar field by occupation, study how creators organize skills, then use source context to decide what is worth opening or reusing.
01 Map a field
Browse 23 occupation groups and 867 SOC roles to learn what skills exist in adjacent domains and how they break down real work.
02 Follow creators
Use creator and repository pages to inspect maintained skill collections, recent updates, and source context before trusting a result.
03 Search with sources
Search 1.7M+ collected skills, then use occupation tags, creators, and GitHub source context to decide what is worth opening.
Start with the occupation map, then follow creators and repositories back to real code. SkillMD helps explain why a skill is worth opening, not only what it is named.
Standardizing Agent Capabilities with SKILL.md and Model Context Protocol (MCP)
In the rapidly evolving landscape of artificial intelligence, LLM agents (Large Language Model agents) have transitioned from simple text predictors to autonomous problem solvers. To orchestrate complex, multi-step agentic workflows, developers require a standardized format to specify agent capabilities, prompt instructions, system rules, and database bindings. This is where SKILL.md and the Model Context Protocol (MCP) have emerged as standard developer paradigms. SkillMD serves as the central directory for indexing, exploring, and sharing these critical agent configurations.
Our open-source registry currently tracks over 1.7 million collected SKILL.md configurations and system prompts. By compiling agent configurations from active developers on GitHub, we bridge the gap between prompt engineering research and production execution. Whether you are building agents with Anthropic's Claude Code, OpenAI's GPT-4, Google's Gemini, or local models using Ollama and LlamaIndex, standardized skill definitions ensure your agents behave predictably across different runtime environments.
What is the Model Context Protocol (MCP)?
The Model Context Protocol (MCP) is an open-source standard designed to connect LLMs to data sources, developer tools, and external environments. MCP establishes a bidirectional communication channel between client applications (like Cursor, Claude Desktop, or custom agent systems) and servers hosting data or capabilities. Standardizing instructions via SKILL.md enables LLMs to query databases, read local files, execute terminal commands, and integrate third-party APIs. SkillMD allows you to find ready-to-run MCP servers and prompt instructions for various occupations and technical tasks.
The Structure of a Professional SKILL.md File
A valid SKILL.md configuration is designed to be easily read by humans and parsed by LLMs. It contains precise system instructions, trigger conditions, required parameters, and execution examples. Below is the typical architectural blueprint of a professional agent skill:
- Metadata & Core Scope: Declares the name of the skill, author details, target models, and a description of the capability.
- Triggers & Intent Detection: Details semantic triggers that help the agent decide when to invoke this skill.
- System Prompts: Explicit system-level instructions that direct the agent's behavior, personality, safety guardrails, and formatting preferences.
- Capabilities & Tools: Lists the files, databases, or APIs the agent must access to complete the tasks.
- Few-Shot Examples: Demonstrates real inputs and outputs, helping the model generalize behavior through in-context learning.
Optimizing Agent Workflows for Modern LLMs
Writing effective agent skills requires deep knowledge of prompt engineering. With the release of advanced reasoning models like Claude 3.5 Sonnet, ChatGPT o1, and DeepSeek-V3, prompt templates must focus on structured thinking. Developers are encouraged to use XML tags (e.g., <thought>, <context>, and <rules>) to isolate execution boundaries. Standardized prompts prevent agents from suffering from context drift, ensuring that long-running tasks remain aligned with the initial system parameters.
Exploring by SOC Occupations and Creator Profiles
What makes SkillMD unique is its taxonomy. Instead of simple text search, we parse and organize files according to the Standard Occupational Classification (SOC) system. This means you can discover skills written for Computer and Mathematical roles, Business and Financial operations, Legal, Design, and and Educational Instruction fields. By tracking creator profiles, developers can study how different teams organize their custom instructions, compare version updates, and fork public configs for specialized enterprise use cases.
SkillMD operates as a high-performance index running on a fast Go backend and a highly responsive Astro SSR frontend. All search queries execute in milliseconds, featuring smart debouncing to prevent multiple API requests while keeping user data secure. Join our community of developers to standardize your AI agent instructions and optimize your LLM prompting workflows today.
Frequently Asked Questions
A practical guide to agent skills: what they are, how to inspect them, and how SkillMD helps you explore the ecosystem.